tf.contrib.rnn.AttentionCellWrapper.__init__(cell, attn_length, attn_size=None, attn_vec_size=None, input_size=None, state_is_tuple=False) Create a cell with attention. Args: cell: an RNNCell, an attention is added to it. attn_length: integer, the size of an attention window. attn_size: integer, the size of an attention vector. Equal to cell.output_size by default. attn_vec_size: integer, the number of convolutional features calculated on attention state and a size of the hidden layer buil

class tf.contrib.rnn.AttentionCellWrapper Basic attention cell wrapper. Implementation based on https://arxiv.org/pdf/1601.06733.pdf.

tf.contrib.metrics.streaming_sparse_recall_at_k(*args, **kwargs) Computes recall@k of the predictions with respect to sparse labels. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). If class_id is specified, we calculate recall by considering only the entries in the batch for which class_id

tf.contrib.metrics.streaming_sparse_average_precision_at_k(predictions, labels, k, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes average precision@k of predictions with respect to sparse labels. See sparse_average_precision_at_k for details on formula. weights are applied to the result of sparse_average_precision_at_k streaming_sparse_average_precision_at_k creates two local variables, average_precision_at_<k>/count and average_precision_at_<k&g

tf.contrib.metrics.streaming_sparse_precision_at_k(*args, **kwargs) Computes precision@k of the predictions with respect to sparse labels. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). If class_id is specified, we calculate precision by considering only the entries in the batch for which

tf.contrib.metrics.streaming_sensitivity_at_specificity(predictions, labels, specificity, weights=None, num_thresholds=200, metrics_collections=None, updates_collections=None, name=None) Computes the the specificity at a given sensitivity. The streaming_sensitivity_at_specificity function creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the sensitivity at the given specificity value. The threshold for the given specifici

tf.contrib.metrics.streaming_recall_at_k(*args, **kwargs) Computes the recall@k of the predictions with respect to dense labels. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). The streaming_recall_at_k function creates two local variables, total and count, that are used to compute the rec

tf.contrib.metrics.streaming_root_mean_squared_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the root mean squared error between the labels and predictions. The streaming_root_mean_squared_error function creates two local variables, total and count that are used to compute the root mean squared error. This average is weighted by weights, and it is ultimately returned as root_mean_squared_error: an idempotent operation that take

tf.contrib.metrics.streaming_recall(*args, **kwargs) Computes the recall of the predictions with respect to the labels. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). The streaming_recall function creates two local variables, true_positives and false_negatives, that are used to compute th

tf.contrib.metrics.streaming_precision(*args, **kwargs) Computes the precision of the predictions with respect to the labels. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). The streaming_precision function creates two local variables, true_positives and false_positives, that are used to c